safety and feasibility of an exonet for supination assistance for stroke survivors

Conditions: Stroke; Cerebral Vascular Accident

Summary: the purpose of this study is to evaluate the safety, feasibility, and efficacy of an exoskeletal network of passive, multi-joint springs for forearm supination. also known as the forearm exonet, the device is a passive, robotic device that will properly assist forearm supination in the post-stroke adult population.

the exonet, a passive robotic solution that provides a soft, biomimetic, and elastic alternative to robotics that embodies intelligence within the mechanical design. several groups have been exploring performance enhancement using springs with custom-tuned parameters via optimization. here, it is possible to have a simple reconfigurable system that can not only assist performance, but can also make training easier, faster, and more complete. this contribution has the potential to be clinically significant for rehabilitating neurologically impaired individuals because this proposal will investigate how motor learning can be facilitated through novel assistive technology. the primary objective of this study is to evaluate the safety, feasibility, and efficacy of using the forearm exonet. specifically, investigators would like to see if the forearm exonet tuned to assistance will lead to a reduction in forearm muscle activity and an increase in active supination range of motion. to accomplish this, we plan to have participants perform upper extremity activities of daily living requiring active forearm supination wearing the exonet. to achieve these goals, we will use a wearable surface electromyography (emg) and inertial measurement unit (imu) using delsys wearable sensors on the forearm muscles. investigators hypothesize that individuals with post-stroke arm movement deficits will experience gains in action research arm test (arat) measures that are significantly above their baseline levels while using the forearm exonet tuned to supination assistive support. secondarily, investigators hypothesize that a forearm exonet tuned to supination assistive support will lead to a significant reduction in arm muscle activity and no significant difference in range of motion across a series of upper-extremity tasks in adults without a history of stroke. lastly, it is hypothesized that usage of a forearm exonet tuned to supination anti-assistance can be safe, feasible and tolerated by patients in a given treatment session.

Eligibility criteria:
inclusion criteria: 1. between the ages of 40-70 (to reduce confounding effects of aging on muscle, movement accuracy and proprioception) 2. have sustained a single, unilateral stroke at least 8 months prior to enrollment 3. severe to moderate upper extremity impairment (arat score 0-30) 4. ability to move their elbow and wrist when supported against gravity 5. cortical stroke with hemiparesis, tactile sensation 6. available medical records about lesion locations indicating the stroke was caused by a middle cerebral artery ischemic infarct exclusion criteria: 1. bilateral paresis 2. diffuse/multiple lesion sites or multiple stroke events 3. hemispatial neglect or visual field cut that prevent visual feedback 4. shoulder pain and/or articular rigidity on the upper limb joint 5. severe sensory deficits indicated by the two-point discrimination test 6. botox injection to the affected upper extremity within the previous 4 months 7. aphasia, cognitive impairment, or affective dysfunction that would influence the ability to consent, perform the experiment, or follow commands 8. concurrent participation in upper extremity rehabilitation either as part of a research intervention protocol or a prescribed therapy 9. other neurological issues 10. meet any of the contraindications to delsys trigno sensors: * implanted with electronic devices of any kind, including cardiac pace-makers or similar assistive devices, electronic induction pumps, and implanted stimulators * irritated skin or open wounds * silver allergy

Keywords: upper extremity rehabilitation stroke